Bayesian Optimization with Tree-structured Dependencies

By Rodolphe Jenatton, Cédric Archambeau, Javier I. González, Matthias W. Seeger. 2017

58 citations recorded by the source corpus.

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Citations in the corpus

Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, methodology): “TreeBO [15]: a BO method which only searches over feed forward structures.”
Efficient Neural Architecture Search with Network Morphism (2018, background): “TreeBO [12] has proposed a way to maximize the acquisition function in a tree-structured parameter space.”
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (2018, methodology): “, 2014), and similarly in (Jenatton et al., 2017), where a BO algorithm for domains with treestructured dependencies was proposed.”
Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start (2017, methodology): “We compare to random Fourier basis expansions [17] as they have already been successfully applied to BO [11, 12].”
Automatic Discovery of Privacy–Utility Pareto Fronts (2019, background): “It has become increasingly relevant in machine learning due to its success in the optimization of model hyperparameters [41, 21].”
Bayesian Optimization for Adaptive Experimental Design: A Review (2020, unknown): “In [67] categorical variables are included by one-hot-encoding alongside numerical variables.”
Auto-Keras: Efficient Neural Architecture Search with Network Morphism (2018, background): “TreeBO (Jenatton et al., 2017) has proposed a way to maximize the acquisition function in a tree-structured parameter space.”
Learnable Embedding Space for Efficient Neural Architecture Compression (2019, background): “Mendoza et al. (2016), Jenatton et al. (2017) and Zela et al. (2018) also fall into this category.”